Gender: Male 
Age: 35-44
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Christian 
Education: High school or below
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 6
Home: Own 
Employment: Employed full time
Occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Mother's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Father's occupation: Technicians and associate professionals (those who apply scientific or artistic concepts and operational methods, e.g. secretaries, fitness workers, lab technicians)
Hobbies: Use social media;Attend football matches 
Devices used daily: Laptop;Tablet;Smartphone;Smartwatch 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Text-to-Speech (i.e, computers being able to read out loud written language) 
NLP tools they would find useful: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Grammar checker (i.e., correcting grammar mistakes);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
Frequency of LLM use: Every day
LLMs used: ChatGPT;Google Gemini;Microsoft Bing AI 
LLM use cases: Proofreading/Editing (e.g., grammar, spelling, structure improvements);Writing (e.g., composing emails);Brainstorming;Solving math or logical problems;Analyze data;Generic chatbot conversation;Completing assignments;Answering questions about general knowledge;Summarizing long texts 
Contexts of LLM use: Work;Entertainment;Personal;Technical;Learning 
Prompt 1: What is the weather?
Prompt 2: Did Tampa Bay win?
Prompt 3: Did the Steelers win?
Prompt 4: Did the chiefs win?
Prompt 5: What was the score
Prompt 6: Did the eagles win
Prompt 7: Did the bills win
Prompt 8: Did Ohio state win
Prompt 9: Did Notre Dame win
Prompt 10: Who won the college playoff game
Comments: No